Distance to baseline
Bray-Curtis distance
Mean shift from baseline samples [-7, 0] measured with Bray-Curtis distance.
abx_bray_to_baseline <- bray.df %>%
filter(Abx_RelDay_1 >= -7 & Abx_RelDay_1 <= 0) %>%
group_by(Subject, Group, Abx_RelDay_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(Abx_RelDay = Abx_RelDay_2) %>%
left_join(SMP)
diet_bray_to_baseline <- bray.df %>%
filter(Diet_RelDay_1 >= -7 & Diet_RelDay_1 <= 0) %>%
group_by(Subject, Group, Diet_RelDay_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(Diet_RelDay = Diet_RelDay_2) %>%
left_join(SMP)
cc_bray_to_baseline <- bray.df %>%
filter(CC_RelDay_1 >= -7 & CC_RelDay_1 <= 0) %>%
group_by(Subject, Group, CC_RelDay_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(CC_RelDay = CC_RelDay_2) %>%
left_join(SMP)
noItv_bray_to_baseline <- bray.df %>%
filter(Group == "NoIntv", DaysFromStart_1 >= 0, DaysFromStart_1 <= 10) %>%
group_by(Subject, Group, DaysFromStart_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(DaysFromStart = DaysFromStart_2) %>%
left_join(SMP) %>%
mutate(Interval = as.character(Interval))
bray_to_baseline <- list(
abx_bray_to_baseline %>% mutate(Interval = Abx_Interval, RelDay = Abx_RelDay) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
diet_bray_to_baseline %>% mutate(Interval = Diet_Interval, RelDay = Diet_RelDay) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
cc_bray_to_baseline %>% mutate(Interval = CC_Interval, RelDay = CC_RelDay) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
noItv_bray_to_baseline %>% mutate(RelDay = DaysFromStart) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline)
)
names(bray_to_baseline) <- c("Abx", "Diet", "CC", "NoIntv")
bray_to_baseline <- plyr::ldply(bray_to_baseline, function(df) df, .id = "perturbation") %>%
mutate(Interval = factor(Interval, levels = names(intv_cols)))
# dim(abx_bray_to_baseline)
# dim(diet_bray_to_baseline)
# dim(cc_bray_to_baseline)
# dim(noItv_bray_to_baseline)
# head(abx_bray_to_baseline)
# head(diet_bray_to_baseline)
# head(cc_bray_to_baseline)
# head(noItv_bray_to_baseline)
save(list = c("brayD.ihs", "bray.df", "bray_to_baseline",
"jaccardD", "jacc.df",
"uniFracD.lst", "uniFrac.df"),
file = "output/pairwise_dist_to_baseline_subj_16S.rda")
bray_to_baseline %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.2) +
scale_color_manual(values = intv_cols) +
theme(legend.position = "bottom") +
facet_wrap(~ perturbation, scales = "free_x") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from perturbation") +
ylab("Bray-Curtis distance to baseline")

bray_to_baseline %>%
filter(perturbation != "NoIntv") %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1) +
scale_color_manual(values = intv_cols) +
theme(legend.position = "bottom") +
facet_wrap(~ perturbation, scales = "free_x", ncol = 3) +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from perturbation") +
ylab("Bray-Curtis distance to baseline")

bray_to_baseline %>%
filter(perturbation != "NoIntv", RelDay <=30, RelDay >= -30) %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1) +
scale_color_manual(values = intv_cols) +
theme(legend.position = "bottom") +
facet_wrap(~ perturbation, ncol = 3) +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from perturbation") +
ylab("Bray-Curtis distance to baseline")

abx_bray_to_baseline %>%
mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
filter(Abx_RelDay >= -50) %>%
ggplot(
aes(x = Abx_RelDay, y = dist_to_baseline,
group = Subject, color = Abx_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.2) +
scale_color_manual(values = abx_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from initial antibiotic dose") +
ylab("Bray-Curtis distance to 7 pre-antibiotic samples")

abx_bray_to_baseline %>%
mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
filter(Abx_RelDay <= 30, Abx_RelDay >= -30) %>%
ggplot(
aes(x = Abx_RelDay, y = dist_to_baseline,
group = Subject, color = Abx_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.5) +
scale_color_manual(values = abx_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from initial antibiotic dose") +
ylab("Bray-Curtis distance to 7 pre-antibiotic samples") +
ylim(0.05, 0.85)

diet_bray_to_baseline %>%
mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
filter(Diet_RelDay <= 50) %>%
ggplot(
aes(x = Diet_RelDay, y = dist_to_baseline,
group = Subject, color = Diet_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = diet_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from diet initiation") +
ylab("Bray-Curtis distance to 7 pre-diet samples")

diet_bray_to_baseline %>%
mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
filter(Diet_RelDay <= 30, Diet_RelDay >= -30) %>%
ggplot(
aes(x = Diet_RelDay, y = dist_to_baseline,
group = Subject, color = Diet_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = diet_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from diet initiation") +
ylab("Bray-Curtis distance to 7 pre-diet samples") +
ylim(0.05, 0.85)

cc_bray_to_baseline %>%
mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
filter(CC_RelDay >= -50 , CC_RelDay <= 50) %>%
ggplot(
aes(x = CC_RelDay, y = dist_to_baseline,
group = Subject, color = CC_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = cc_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from colon cleanout") +
ylab("Bray-Curtis distance to 7 pre-colon-cleanout samples")

cc_bray_to_baseline %>%
mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
filter(CC_RelDay >= -30 , CC_RelDay <= 30) %>%
ggplot(
aes(x = CC_RelDay, y = dist_to_baseline,
group = Subject, color = CC_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = cc_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from colon cleanout") +
ylab("Bray-Curtis distance to 7 pre-colon-cleanout samples") +
ylim(0.05, 0.85)

Jaccard distance
abx_jacc_to_baseline <- jacc.df %>%
filter(Abx_RelDay_1 >= -7 & Abx_RelDay_1 <= 0) %>%
group_by(Subject, Group, Abx_RelDay_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(Abx_RelDay = Abx_RelDay_2) %>%
left_join(SMP)
diet_jacc_to_baseline <- jacc.df %>%
filter(Diet_RelDay_1 >= -7 & Diet_RelDay_1 <= 0) %>%
group_by(Subject, Group, Diet_RelDay_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(Diet_RelDay = Diet_RelDay_2) %>%
left_join(SMP)
cc_jacc_to_baseline <- jacc.df %>%
filter(CC_RelDay_1 >= -7 & CC_RelDay_1 <= 0) %>%
group_by(Subject, Group, CC_RelDay_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(CC_RelDay = CC_RelDay_2) %>%
left_join(SMP)
noItv_jacc_to_baseline <- jacc.df %>%
filter(Group == "NoIntv", DaysFromStart_1 >= 0, DaysFromStart_1 <= 10) %>%
group_by(Subject, Group, DaysFromStart_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(DaysFromStart = DaysFromStart_2) %>%
left_join(SMP) %>%
mutate(Interval = as.character(Interval))
jacc_to_baseline <- list(
abx_jacc_to_baseline %>% mutate(Interval = Abx_Interval, RelDay = Abx_RelDay) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
diet_jacc_to_baseline %>% mutate(Interval = Diet_Interval, RelDay = Diet_RelDay) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
cc_jacc_to_baseline %>% mutate(Interval = CC_Interval, RelDay = CC_RelDay) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
noItv_jacc_to_baseline %>% mutate(RelDay = DaysFromStart) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline)
)
names(jacc_to_baseline) <- c("Abx", "Diet", "CC", "NoIntv")
jacc_to_baseline <- plyr::ldply(jacc_to_baseline, function(df) df, .id = "perturbation") %>%
mutate(Interval = factor(Interval, levels = names(intv_cols)))
save(list = c("brayD.ihs", "bray.df", "bray_to_baseline",
"jaccardD", "jacc.df", "jacc_to_baseline",
"uniFracD.lst", "uniFrac.df"),
file = "output/pairwise_dist_to_baseline_subj_16S.rda")
jacc_to_baseline %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.2) +
scale_color_manual(values = intv_cols) +
theme(legend.position = "bottom") +
facet_wrap(~ perturbation, scales = "free_x") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from perturbation") +
ylab("Jaccard distance to baseline")

jacc_to_baseline %>%
filter(perturbation != "NoIntv") %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1) +
scale_color_manual(values = intv_cols) +
theme(legend.position = "bottom") +
facet_wrap(~ perturbation, scales = "free_x", ncol = 3) +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from perturbation") +
ylab("Jaccard distance to baseline")

jacc_to_baseline %>%
filter(perturbation != "NoIntv", RelDay <=30, RelDay >= -30) %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1) +
scale_color_manual(values = intv_cols) +
theme(legend.position = "bottom") +
facet_wrap(~ perturbation, ncol = 3) +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from perturbation") +
ylab("Jaccard distance to baseline")

abx_jacc_to_baseline %>%
mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
filter(Abx_RelDay >= -50) %>%
ggplot(
aes(x = Abx_RelDay, y = dist_to_baseline,
group = Subject, color = Abx_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.2) +
scale_color_manual(values = abx_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from initial antibiotic dose") +
ylab("Jaccard distance to 7 pre-antibiotic samples")

abx_jacc_to_baseline %>%
mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
filter(Abx_RelDay <= 30, Abx_RelDay >= -30) %>%
ggplot(
aes(x = Abx_RelDay, y = dist_to_baseline,
group = Subject, color = Abx_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.5) +
scale_color_manual(values = abx_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from initial antibiotic dose") +
ylab("Jaccard distance to 7 pre-antibiotic samples") +
ylim(0.1, 1.0)

diet_jacc_to_baseline %>%
mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
filter(Diet_RelDay <= 50) %>%
ggplot(
aes(x = Diet_RelDay, y = dist_to_baseline,
group = Subject, color = Diet_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = diet_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from diet initiation") +
ylab("Jaccard distance to 7 pre-diet samples")

diet_jacc_to_baseline %>%
mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
filter(Diet_RelDay <= 30, Diet_RelDay >= -30) %>%
ggplot(
aes(x = Diet_RelDay, y = dist_to_baseline,
group = Subject, color = Diet_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = diet_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from diet initiation") +
ylab("Jaccard distance to 7 pre-diet samples") +
ylim(0.1, 1.0)

cc_jacc_to_baseline %>%
mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
filter(CC_RelDay >= -50 , CC_RelDay <= 50) %>%
ggplot(
aes(x = CC_RelDay, y = dist_to_baseline,
group = Subject, color = CC_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = cc_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from colon cleanout") +
ylab("Jaccard distance to 7 pre-colon-cleanout samples")

cc_jacc_to_baseline %>%
mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
filter(CC_RelDay >= -30 , CC_RelDay <= 30) %>%
ggplot(
aes(x = CC_RelDay, y = dist_to_baseline,
group = Subject, color = CC_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = cc_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from colon cleanout") +
ylab("Jaccard distance to 7 pre-colon-cleanout samples") +
ylim(0.1, 1.0)

(Unweighted) UniFrac
abx_uniFrac_to_baseline <- uniFrac.df %>%
filter(Abx_RelDay_1 >= -7 & Abx_RelDay_1 <= 0) %>%
group_by(Subject, Group, Abx_RelDay_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(Abx_RelDay = Abx_RelDay_2) %>%
left_join(SMP)
diet_uniFrac_to_baseline <- uniFrac.df %>%
filter(Diet_RelDay_1 >= -7 & Diet_RelDay_1 <= 0) %>%
group_by(Subject, Group, Diet_RelDay_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(Diet_RelDay = Diet_RelDay_2) %>%
left_join(SMP)
cc_uniFrac_to_baseline <- uniFrac.df %>%
filter(CC_RelDay_1 >= -7 & CC_RelDay_1 <= 0) %>%
group_by(Subject, Group, CC_RelDay_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(CC_RelDay = CC_RelDay_2) %>%
left_join(SMP)
noItv_uniFrac_to_baseline <- uniFrac.df %>%
filter(Group == "NoIntv", DaysFromStart_1 >= 0, DaysFromStart_1 <= 10) %>%
group_by(Subject, Group, DaysFromStart_2) %>%
summarize(dist_to_baseline = mean(dist)) %>%
rename(DaysFromStart = DaysFromStart_2) %>%
left_join(SMP) %>%
mutate(Interval = as.character(Interval))
uniFrac_to_baseline <- list(
abx_uniFrac_to_baseline %>% mutate(Interval = Abx_Interval, RelDay = Abx_RelDay) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
diet_uniFrac_to_baseline %>% mutate(Interval = Diet_Interval, RelDay = Diet_RelDay) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
cc_uniFrac_to_baseline %>% mutate(Interval = CC_Interval, RelDay = CC_RelDay) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
noItv_uniFrac_to_baseline %>% mutate(RelDay = DaysFromStart) %>%
select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline)
)
names(uniFrac_to_baseline) <- c("Abx", "Diet", "CC", "NoIntv")
uniFrac_to_baseline <- plyr::ldply(uniFrac_to_baseline, function(df) df, .id = "perturbation") %>%
mutate(Interval = factor(Interval, levels = names(intv_cols)))
save(list = c("brayD.ihs", "bray.df", "bray_to_baseline",
"jaccardD", "jacc.df", "jacc_to_baseline",
"uniFracD.lst", "uniFrac.df", "uniFrac_to_baseline"),
file = "output/pairwise_dist_to_baseline_subj_16S.rda")
uniFrac_to_baseline %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.2) +
scale_color_manual(values = intv_cols) +
theme(legend.position = "bottom") +
facet_wrap(~ perturbation, scales = "free_x") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from perturbation") +
ylab("UniFrac distance to baseline")

uniFrac_to_baseline %>%
filter(perturbation != "NoIntv") %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1) +
scale_color_manual(values = intv_cols) +
theme(legend.position = "bottom") +
facet_wrap(~ perturbation, scales = "free_x", ncol = 3) +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from perturbation") +
ylab("UniFrac distance to baseline")

uniFrac_to_baseline %>%
filter(perturbation != "NoIntv", RelDay <=30, RelDay >= -30) %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1) +
scale_color_manual(values = intv_cols) +
theme(legend.position = "bottom") +
facet_wrap(~ perturbation, ncol = 3) +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from perturbation") +
ylab("UniFrac distance to baseline")

abx_uniFrac_to_baseline %>%
mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
filter(Abx_RelDay >= -50) %>%
ggplot(
aes(x = Abx_RelDay, y = dist_to_baseline,
group = Subject, color = Abx_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.2) +
scale_color_manual(values = abx_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from initial antibiotic dose") +
ylab("UniFrac distance to 7 pre-antibiotic samples")

abx_uniFrac_to_baseline %>%
mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
filter(Abx_RelDay <= 30, Abx_RelDay >= -30) %>%
ggplot(
aes(x = Abx_RelDay, y = dist_to_baseline,
group = Subject, color = Abx_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.5) +
scale_color_manual(values = abx_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from initial antibiotic dose") +
ylab("UniFrac distance to 7 pre-antibiotic samples") +
ylim(0.1, 1.0)

diet_uniFrac_to_baseline %>%
mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
filter(Diet_RelDay <= 50) %>%
ggplot(
aes(x = Diet_RelDay, y = dist_to_baseline,
group = Subject, color = Diet_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = diet_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from diet initiation") +
ylab("UniFrac distance to 7 pre-diet samples")

diet_uniFrac_to_baseline %>%
mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
filter(Diet_RelDay <= 30, Diet_RelDay >= -30) %>%
ggplot(
aes(x = Diet_RelDay, y = dist_to_baseline,
group = Subject, color = Diet_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = diet_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from diet initiation") +
ylab("UniFrac distance to 7 pre-diet samples") +
ylim(0.1, 1.0)

cc_uniFrac_to_baseline %>%
mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
filter(CC_RelDay >= -50 , CC_RelDay <= 50) %>%
ggplot(
aes(x = CC_RelDay, y = dist_to_baseline,
group = Subject, color = CC_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = cc_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from colon cleanout") +
ylab("UniFrac distance to 7 pre-colon-cleanout samples")

cc_uniFrac_to_baseline %>%
mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
filter(CC_RelDay >= -30 , CC_RelDay <= 30) %>%
ggplot(
aes(x = CC_RelDay, y = dist_to_baseline,
group = Subject, color = CC_Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.7, size = 1.2) +
scale_color_manual(values = cc_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from colon cleanout") +
ylab("UniFrac distance to 7 pre-colon-cleanout samples") +
ylim(0.1, 1.0)

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /share/software/user/open/openblas/0.2.19/lib/libopenblasp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] decontam_1.2.1 rmarkdown_1.14 BiocStyle_2.10.0 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3
[7] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.3 ggplot2_3.2.0 tidyverse_1.2.1.9000
[13] RColorBrewer_1.1-2 phyloseq_1.26.1
loaded via a namespace (and not attached):
[1] nlme_3.1-140 fs_1.3.1 lubridate_1.7.4 httr_1.4.0 tools_3.5.1 backports_1.1.4
[7] R6_2.4.0 vegan_2.5-5 DBI_1.0.0 lazyeval_0.2.2 BiocGenerics_0.28.0 mgcv_1.8-28
[13] colorspace_1.4-1 permute_0.9-5 ade4_1.7-13 withr_2.1.2 tidyselect_0.2.5 compiler_3.5.1
[19] cli_1.1.0 rvest_0.3.4 Biobase_2.42.0 xml2_1.2.0 labeling_0.3 scales_1.0.0
[25] digest_0.6.20 XVector_0.22.0 base64enc_0.1-3 pkgconfig_2.0.2 htmltools_0.3.6 dbplyr_1.4.2
[31] highr_0.8 rlang_0.4.0 readxl_1.3.1 rstudioapi_0.10 generics_0.0.2 jsonlite_1.6
[37] magrittr_1.5 biomformat_1.10.1 Matrix_1.2-17 Rcpp_1.0.1 munsell_0.5.0 S4Vectors_0.20.1
[43] Rhdf5lib_1.4.3 ape_5.3 stringi_1.4.3 yaml_2.2.0 MASS_7.3-51.4 zlibbioc_1.28.0
[49] rhdf5_2.26.2 plyr_1.8.4 grid_3.5.1 parallel_3.5.1 crayon_1.3.4 lattice_0.20-38
[55] Biostrings_2.50.2 haven_2.1.1 splines_3.5.1 multtest_2.38.0 hms_0.5.0 zeallot_0.1.0
[61] knitr_1.23 pillar_1.4.2 igraph_1.2.4.1 reshape2_1.4.3 codetools_0.2-16 stats4_3.5.1
[67] reprex_0.3.0 glue_1.3.1 evaluate_0.14 data.table_1.12.2 BiocManager_1.30.4 modelr_0.1.4
[73] vctrs_0.2.0 foreach_1.4.4 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.8
[79] broom_0.5.2 survival_2.44-1.1 viridisLite_0.3.0 iterators_1.0.10 IRanges_2.16.0 cluster_2.1.0
---
title: "Response to perturbations"
output: html_notebook
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library("phyloseq")
library("RColorBrewer")
library("tidyverse")
datadir <- "../../data/"
curdir <- getwd()
theme_set(theme_bw())
theme_update(text = element_text(20))

abx_intv_cols <- c("PreAbx" = "grey60", "MidAbx" = "#E41A1C", 
                   "PostAbx" = "#00BFC4", "UnpAbx" = "Purple")

diet_intv_cols <- c("PreDiet" = "grey60", "MidDiet" = "#FD8D3C", "PostDiet" = "#4DAF4A")
cc_intv_cols <- c("PreCC" = "grey60", "PostCC" = "#7A0177") #"#AE017E")

intv_cols <- c(abx_intv_cols, diet_intv_cols, cc_intv_cols, "NoInterv" = "grey60")
```



# Load 

```{r}
# File generated in /perturbation_16s/analysis/analysis_summer2019/generate_phyloseq.rmd
psSubj <- readRDS("../../data/16S/phyloseq/perturb_physeq_participants_decontam_15Jul19.rds")
psSubj
# otu_table()   OTU Table:         [ 2425 taxa and 4402 samples ]
# sample_data() Sample Data:       [ 4402 samples by 40 sample variables ]
SMP <- data.frame(sample_data(psSubj))
SUBJ <- SMP %>% select(Subject, Age:BirthYear) %>% distinct()
```

```{r}
SMP %>%
    select(Group, Subject) %>%
    distinct() %>%
    group_by(Group) %>%
    summarise(subject_count = n())
```

```{r}
load("output/pairwise_dist_subj_16S.rda")
ls()
```


# Distance to baseline

## Bray-Curtis distance

Mean shift from baseline samples [-7, 0] measured with Bray-Curtis distance.

```{r, eval = FALSE}
abx_bray_to_baseline <- bray.df %>%
  filter(Abx_RelDay_1 >= -7 & Abx_RelDay_1 <= 0) %>%
  group_by(Subject, Group, Abx_RelDay_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(Abx_RelDay = Abx_RelDay_2) %>%
  left_join(SMP)

diet_bray_to_baseline <- bray.df %>%
  filter(Diet_RelDay_1 >= -7 & Diet_RelDay_1 <= 0) %>%
  group_by(Subject, Group, Diet_RelDay_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(Diet_RelDay = Diet_RelDay_2) %>%
  left_join(SMP)

cc_bray_to_baseline <- bray.df %>%
  filter(CC_RelDay_1 >= -7 & CC_RelDay_1 <= 0) %>%
  group_by(Subject, Group, CC_RelDay_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(CC_RelDay = CC_RelDay_2) %>%
  left_join(SMP)


noItv_bray_to_baseline <- bray.df %>%
  filter(Group == "NoIntv", DaysFromStart_1 >= 0, DaysFromStart_1 <= 10) %>%
  group_by(Subject, Group, DaysFromStart_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(DaysFromStart = DaysFromStart_2) %>%
  left_join(SMP) %>%
  mutate(Interval = as.character(Interval))

bray_to_baseline <- list(
  abx_bray_to_baseline %>% mutate(Interval = Abx_Interval, RelDay = Abx_RelDay) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
  diet_bray_to_baseline %>% mutate(Interval = Diet_Interval, RelDay = Diet_RelDay) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
  cc_bray_to_baseline %>% mutate(Interval = CC_Interval, RelDay = CC_RelDay) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
  noItv_bray_to_baseline %>% mutate(RelDay = DaysFromStart) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline)
)
names(bray_to_baseline) <- c("Abx", "Diet", "CC", "NoIntv")
bray_to_baseline <- plyr::ldply(bray_to_baseline, function(df) df, .id = "perturbation") %>%
  mutate(Interval = factor(Interval, levels = names(intv_cols)))

# dim(abx_bray_to_baseline)  
# dim(diet_bray_to_baseline) 
# dim(cc_bray_to_baseline) 
# dim(noItv_bray_to_baseline)
# head(abx_bray_to_baseline)  
# head(diet_bray_to_baseline)  
# head(cc_bray_to_baseline)  
# head(noItv_bray_to_baseline)  


save(list = c("brayD.ihs", "bray.df", "bray_to_baseline",
              "jaccardD", "jacc.df",
              "uniFracD.lst", "uniFrac.df"),
     file = "output/pairwise_dist_to_baseline_subj_16S.rda")

```


```{r bray-to-baseline, fig.width=8}
bray_to_baseline %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.2) + 
  scale_color_manual(values = intv_cols) + 
  theme(legend.position = "bottom") + 
  facet_wrap(~ perturbation, scales = "free_x") +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  perturbation") +
  ylab("Bray-Curtis distance to baseline") 

```

```{r bray-to-baseline-perturb, fig.width=10, fig.height=4}
bray_to_baseline %>%
  filter(perturbation != "NoIntv") %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1) + 
  scale_color_manual(values = intv_cols) + 
  theme(legend.position = "bottom") + 
  facet_wrap(~ perturbation, scales = "free_x", ncol = 3) +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  perturbation") +
  ylab("Bray-Curtis distance to baseline") 

```

```{r bray-to-baseline-perturb-zoom, fig.width=10, fig.height=4}
bray_to_baseline %>%
  filter(perturbation != "NoIntv", RelDay <=30, RelDay >= -30) %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1) + 
  scale_color_manual(values = intv_cols) + 
  theme(legend.position = "bottom") + 
  facet_wrap(~ perturbation, ncol = 3) +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  perturbation") +
  ylab("Bray-Curtis distance to baseline") 

```

```{r abx-bray-to-baseline, fig.width=8}
abx_bray_to_baseline %>%
  mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
  filter(Abx_RelDay >= -50) %>%
  ggplot(
    aes(x = Abx_RelDay, y = dist_to_baseline, 
        group = Subject, color = Abx_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.2) + 
  scale_color_manual(values = abx_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from initial antibiotic dose") +
  ylab("Bray-Curtis distance to 7 pre-antibiotic samples") 
```


```{r abx-bray-to-baseline-zoom, fig.width=8}
abx_bray_to_baseline %>%
  mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
  filter(Abx_RelDay <= 30, Abx_RelDay >= -30) %>%
  ggplot(
    aes(x = Abx_RelDay, y = dist_to_baseline, 
        group = Subject, color = Abx_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.5) + 
  scale_color_manual(values = abx_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from initial antibiotic dose") +
  ylab("Bray-Curtis distance to 7 pre-antibiotic samples") +
  ylim(0.05, 0.85)

```

```{r diet-bray-to-baseline, fig.width=8}

diet_bray_to_baseline %>%
  mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
  filter(Diet_RelDay <= 50) %>%
  ggplot(
    aes(x = Diet_RelDay, y = dist_to_baseline, 
        group = Subject, color = Diet_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = diet_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from diet initiation") +
  ylab("Bray-Curtis distance to 7 pre-diet samples") 

```


```{r diet-bray-to-baseline-zoom, fig.width=8}
diet_bray_to_baseline %>%
  mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
  filter(Diet_RelDay <= 30, Diet_RelDay >= -30) %>%
  ggplot(
    aes(x = Diet_RelDay, y = dist_to_baseline, 
        group = Subject, color = Diet_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = diet_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from diet initiation") +
  ylab("Bray-Curtis distance to 7 pre-diet samples") +
  ylim(0.05, 0.85)

```

```{r cc-bray-to-baseline, fig.width=8}
cc_bray_to_baseline %>%
  mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
  filter(CC_RelDay >= -50 , CC_RelDay <= 50) %>%
  ggplot(
    aes(x = CC_RelDay, y = dist_to_baseline, 
        group = Subject, color = CC_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = cc_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from colon cleanout") +
  ylab("Bray-Curtis distance to 7 pre-colon-cleanout samples")

```

```{r cc-bray-to-baseline-zoom, fig.width=8}
cc_bray_to_baseline %>%
  mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
 filter(CC_RelDay >= -30 , CC_RelDay <= 30) %>%
  ggplot(
    aes(x = CC_RelDay, y = dist_to_baseline, 
        group = Subject, color = CC_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = cc_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from colon cleanout") +
  ylab("Bray-Curtis distance to 7 pre-colon-cleanout samples") +
  ylim(0.05, 0.85)

```


## Jaccard distance

```{r, eval = FALSE}
abx_jacc_to_baseline <- jacc.df %>%
  filter(Abx_RelDay_1 >= -7 & Abx_RelDay_1 <= 0) %>%
  group_by(Subject, Group, Abx_RelDay_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(Abx_RelDay = Abx_RelDay_2) %>%
  left_join(SMP)

diet_jacc_to_baseline <- jacc.df %>%
  filter(Diet_RelDay_1 >= -7 & Diet_RelDay_1 <= 0) %>%
  group_by(Subject, Group, Diet_RelDay_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(Diet_RelDay = Diet_RelDay_2) %>% 
  left_join(SMP)

cc_jacc_to_baseline <- jacc.df %>%
  filter(CC_RelDay_1 >= -7 & CC_RelDay_1 <= 0) %>%
  group_by(Subject, Group, CC_RelDay_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(CC_RelDay = CC_RelDay_2) %>%
  left_join(SMP)


noItv_jacc_to_baseline <- jacc.df %>%
  filter(Group == "NoIntv", DaysFromStart_1 >= 0, DaysFromStart_1 <= 10) %>%
  group_by(Subject, Group, DaysFromStart_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(DaysFromStart = DaysFromStart_2) %>%
  left_join(SMP) %>%
  mutate(Interval = as.character(Interval))

jacc_to_baseline <- list(
  abx_jacc_to_baseline %>% mutate(Interval = Abx_Interval, RelDay = Abx_RelDay) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
  diet_jacc_to_baseline %>% mutate(Interval = Diet_Interval, RelDay = Diet_RelDay) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
  cc_jacc_to_baseline %>% mutate(Interval = CC_Interval, RelDay = CC_RelDay) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
  noItv_jacc_to_baseline %>% mutate(RelDay = DaysFromStart) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline)
)
names(jacc_to_baseline) <- c("Abx", "Diet", "CC", "NoIntv")
jacc_to_baseline <- plyr::ldply(jacc_to_baseline, function(df) df, .id = "perturbation") %>%
  mutate(Interval = factor(Interval, levels = names(intv_cols)))

save(list = c("brayD.ihs", "bray.df", "bray_to_baseline",
              "jaccardD", "jacc.df", "jacc_to_baseline",
              "uniFracD.lst", "uniFrac.df"),
     file = "output/pairwise_dist_to_baseline_subj_16S.rda")

```

```{r jacc-to-baseline, fig.width=8}
jacc_to_baseline %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.2) + 
  scale_color_manual(values = intv_cols) + 
  theme(legend.position = "bottom") + 
  facet_wrap(~ perturbation, scales = "free_x") +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  perturbation") +
  ylab("Jaccard distance to baseline") 

```

```{r jacc-to-baseline-perturb, fig.width=10, fig.height=4}
jacc_to_baseline %>%
  filter(perturbation != "NoIntv") %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1) + 
  scale_color_manual(values = intv_cols) + 
  theme(legend.position = "bottom") + 
  facet_wrap(~ perturbation, scales = "free_x", ncol = 3) +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  perturbation") +
  ylab("Jaccard distance to baseline") 

```

```{r jacc-to-baseline-perturb-zoom, fig.width=10, fig.height=4}
jacc_to_baseline %>%
  filter(perturbation != "NoIntv", RelDay <=30, RelDay >= -30) %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1) + 
  scale_color_manual(values = intv_cols) + 
  theme(legend.position = "bottom") + 
  facet_wrap(~ perturbation, ncol = 3) +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  perturbation") +
  ylab("Jaccard distance to baseline") 

```


```{r abx-jacc-to-baseline, fig.width=8}
abx_jacc_to_baseline %>%
  mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
  filter(Abx_RelDay >= -50) %>%
  ggplot(
    aes(x = Abx_RelDay, y = dist_to_baseline, 
        group = Subject, color = Abx_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.2) + 
  scale_color_manual(values = abx_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from initial antibiotic dose") +
  ylab("Jaccard distance to 7 pre-antibiotic samples")
```


```{r abx-jacc-to-baseline-zoom, fig.width=8}
abx_jacc_to_baseline %>%
  mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
  filter(Abx_RelDay <= 30, Abx_RelDay >= -30) %>%
  ggplot(
    aes(x = Abx_RelDay, y = dist_to_baseline, 
        group = Subject, color = Abx_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.5) + 
  scale_color_manual(values = abx_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from initial antibiotic dose") +
  ylab("Jaccard distance to 7 pre-antibiotic samples") +
  ylim(0.1, 1.0)

```

```{r diet-jacc-to-baseline, fig.width=8}

diet_jacc_to_baseline %>%
  mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
  filter(Diet_RelDay <= 50) %>%
  ggplot(
    aes(x = Diet_RelDay, y = dist_to_baseline, 
        group = Subject, color = Diet_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = diet_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from diet initiation") +
  ylab("Jaccard distance to 7 pre-diet samples")

```


```{r diet-jacc-to-baseline-zoom, fig.width=8}
diet_jacc_to_baseline %>%
  mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
  filter(Diet_RelDay <= 30, Diet_RelDay >= -30) %>%
  ggplot(
    aes(x = Diet_RelDay, y = dist_to_baseline, 
        group = Subject, color = Diet_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = diet_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from diet initiation") +
  ylab("Jaccard distance to 7 pre-diet samples") +
  ylim(0.1, 1.0)

```

```{r cc-jacc-to-baseline, fig.width=8}
cc_jacc_to_baseline %>%
  mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
  filter(CC_RelDay >= -50 , CC_RelDay <= 50) %>%
  ggplot(
    aes(x = CC_RelDay, y = dist_to_baseline, 
        group = Subject, color = CC_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = cc_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from colon cleanout") +
  ylab("Jaccard distance to 7 pre-colon-cleanout samples")

```

```{r cc-jacc-to-baseline-zoom, fig.width=8}
cc_jacc_to_baseline %>%
  mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
 filter(CC_RelDay >= -30 , CC_RelDay <= 30) %>%
  ggplot(
    aes(x = CC_RelDay, y = dist_to_baseline, 
        group = Subject, color = CC_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = cc_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from colon cleanout") +
  ylab("Jaccard distance to 7 pre-colon-cleanout samples") +
  ylim(0.1, 1.0)

```


## (Unweighted) UniFrac


```{r, eval = FALSE}
abx_uniFrac_to_baseline <- uniFrac.df %>%
  filter(Abx_RelDay_1 >= -7 & Abx_RelDay_1 <= 0) %>%
  group_by(Subject, Group, Abx_RelDay_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(Abx_RelDay = Abx_RelDay_2) %>%
  left_join(SMP)

diet_uniFrac_to_baseline <- uniFrac.df %>%
  filter(Diet_RelDay_1 >= -7 & Diet_RelDay_1 <= 0) %>%
  group_by(Subject, Group, Diet_RelDay_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(Diet_RelDay = Diet_RelDay_2) %>% 
  left_join(SMP)

cc_uniFrac_to_baseline <- uniFrac.df %>%
  filter(CC_RelDay_1 >= -7 & CC_RelDay_1 <= 0) %>%
  group_by(Subject, Group, CC_RelDay_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(CC_RelDay = CC_RelDay_2) %>%
  left_join(SMP)


noItv_uniFrac_to_baseline <- uniFrac.df %>%
  filter(Group == "NoIntv", DaysFromStart_1 >= 0, DaysFromStart_1 <= 10) %>%
  group_by(Subject, Group, DaysFromStart_2) %>%
  summarize(dist_to_baseline = mean(dist)) %>%
  rename(DaysFromStart = DaysFromStart_2) %>%
  left_join(SMP) %>%
  mutate(Interval = as.character(Interval))

uniFrac_to_baseline <- list(
  abx_uniFrac_to_baseline %>% mutate(Interval = Abx_Interval, RelDay = Abx_RelDay) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
  diet_uniFrac_to_baseline %>% mutate(Interval = Diet_Interval, RelDay = Diet_RelDay) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
  cc_uniFrac_to_baseline %>% mutate(Interval = CC_Interval, RelDay = CC_RelDay) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline),
  noItv_uniFrac_to_baseline %>% mutate(RelDay = DaysFromStart) %>%
    select(Meas_ID, Subject, Group, Interval, RelDay, dist_to_baseline)
)
names(uniFrac_to_baseline) <- c("Abx", "Diet", "CC", "NoIntv")
uniFrac_to_baseline <- plyr::ldply(uniFrac_to_baseline, function(df) df, .id = "perturbation") %>%
  mutate(Interval = factor(Interval, levels = names(intv_cols)))

save(list = c("brayD.ihs", "bray.df", "bray_to_baseline",
              "jaccardD", "jacc.df", "jacc_to_baseline",
              "uniFracD.lst", "uniFrac.df", "uniFrac_to_baseline"),
     file = "output/pairwise_dist_to_baseline_subj_16S.rda")

```

```{r uniFrac-to-baseline, fig.width=8}
uniFrac_to_baseline %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.2) + 
  scale_color_manual(values = intv_cols) + 
  theme(legend.position = "bottom") + 
  facet_wrap(~ perturbation, scales = "free_x") +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  perturbation") +
  ylab("UniFrac distance to baseline") 

```

```{r uniFrac-to-baseline-perturb, fig.width=10, fig.height=4}
uniFrac_to_baseline %>%
  filter(perturbation != "NoIntv") %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1) + 
  scale_color_manual(values = intv_cols) + 
  theme(legend.position = "bottom") + 
  facet_wrap(~ perturbation, scales = "free_x", ncol = 3) +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  perturbation") +
  ylab("UniFrac distance to baseline") 

```

```{r uniFrac-to-baseline-perturb-zoom, fig.width=10, fig.height=4}
uniFrac_to_baseline %>%
  filter(perturbation != "NoIntv", RelDay <=30, RelDay >= -30) %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1) + 
  scale_color_manual(values = intv_cols) + 
  theme(legend.position = "bottom") + 
  facet_wrap(~ perturbation, ncol = 3) +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  perturbation") +
  ylab("UniFrac distance to baseline") 

```


```{r abx-uniFrac-to-baseline, fig.width=8}
abx_uniFrac_to_baseline %>%
  mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
  filter(Abx_RelDay >= -50) %>%
  ggplot(
    aes(x = Abx_RelDay, y = dist_to_baseline, 
        group = Subject, color = Abx_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.2) + 
  scale_color_manual(values = abx_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from initial antibiotic dose") +
  ylab("UniFrac distance to 7 pre-antibiotic samples")
```


```{r abx-uniFrac-to-baseline-zoom, fig.width=8}
abx_uniFrac_to_baseline %>%
  mutate(Abx_Interval = factor(Abx_Interval, level = names(abx_intv_cols))) %>%
  filter(Abx_RelDay <= 30, Abx_RelDay >= -30) %>%
  ggplot(
    aes(x = Abx_RelDay, y = dist_to_baseline, 
        group = Subject, color = Abx_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.5) + 
  scale_color_manual(values = abx_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from initial antibiotic dose") +
  ylab("UniFrac distance to 7 pre-antibiotic samples") +
  ylim(0.1, 1.0)

```

```{r diet-uniFrac-to-baseline, fig.width=8}

diet_uniFrac_to_baseline %>%
  mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
  filter(Diet_RelDay <= 50) %>%
  ggplot(
    aes(x = Diet_RelDay, y = dist_to_baseline, 
        group = Subject, color = Diet_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = diet_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from  diet initiation") +
  ylab("UniFrac distance to 7 pre-diet samples")

```


```{r diet-uniFrac-to-baseline-zoom, fig.width=8}
diet_uniFrac_to_baseline %>%
  mutate(Diet_Interval = factor(Diet_Interval, level = names(diet_intv_cols))) %>%
  filter(Diet_RelDay <= 30, Diet_RelDay >= -30) %>%
  ggplot(
    aes(x = Diet_RelDay, y = dist_to_baseline, 
        group = Subject, color = Diet_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = diet_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from diet initiation") +
  ylab("UniFrac distance to 7 pre-diet samples") +
  ylim(0.1, 1.0)

```

```{r cc-uniFrac-to-baseline, fig.width=8}
cc_uniFrac_to_baseline %>%
  mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
  filter(CC_RelDay >= -50 , CC_RelDay <= 50) %>%
  ggplot(
    aes(x = CC_RelDay, y = dist_to_baseline, 
        group = Subject, color = CC_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = cc_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from colon cleanout") +
  ylab("UniFrac distance to 7 pre-colon-cleanout samples")

```

```{r cc-uniFrac-to-baseline-zoom, fig.width=8}
cc_uniFrac_to_baseline %>%
  mutate(CC_Interval = factor(CC_Interval, level = names(cc_intv_cols))) %>%
 filter(CC_RelDay >= -30 , CC_RelDay <= 30) %>%
  ggplot(
    aes(x = CC_RelDay, y = dist_to_baseline, 
        group = Subject, color = CC_Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.7, size = 1.2) + 
  scale_color_manual(values = cc_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from colon cleanout") +
  ylab("UniFrac distance to 7 pre-colon-cleanout samples") +
  ylim(0.1, 1.0)

```




```{r}
sessionInfo()
```

